Open Access

ARTICLE

A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM_2.5) Concentration Forecasting

Muhammad Zulqarnain¹, Rozaida Ghazali^1,*, Habib Shah², Lokman Hakim Ismail¹, Abdullah Alsheddy³, Maqsood Mahmud⁴

1 Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia
2 College of Computer Science, King Khalid University, Abha, Saudi Arabia
3 College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
4 Department of Management Information Systems, College of Business Administration, Imam Abdulrahman Bin Faisal University, 31441, Dammam, Saudi Arabia

* Corresponding Author: Rozaida Ghazali. Email:

(This article belongs to the Special Issue: Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories)

Computers, Materials & Continua 2022, 71(2), 3051-3068. https://doi.org/10.32604/cmc.2022.021629

Received 08 July 2021; Accepted 23 August 2021; Issue published 07 December 2021

Abstract

Air pollution is a significant problem in modern societies since it has a serious impact on human health and the environment. Particulate Matter (PM_2.5) is a type of air pollution that contains of interrupted elements with a diameter less than or equal to 2.5 m. For risk assessment and epidemiological investigations, a better knowledge of the spatiotemporal variation of PM_2.5 concentration in a constant space-time area is essential. Conventional spatiotemporal interpolation approaches commonly relying on robust presumption by limiting interpolation algorithms to those with explicit and basic mathematical expression, ignoring a plethora of hidden but crucial manipulating aspects. Many advanced deep learning approaches have been proposed to forecast Particulate Matter (PM_2.5). Recurrent neural network (RNN) is one of the popular deep learning architectures which is widely employed in PM_2.5 concentration forecasting. In this research, we proposed a Two-State Gated Recurrent Unit (TS-GRU) for monitoring and estimating the PM_2.5 concentration forecasting system. The proposed algorithm is capable of considering both spatial and temporal hidden affecting elements spontaneously. We tested our model using data from daily PM_2.5 dimensions taken in the contactual southeast area of the United States in 2009. In the studies, three evaluation matrices were utilized to compare the overall performance of each algorithm: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The experimental results revealed that our proposed TS-GRU model outperformed compared to the other deep learning approaches in terms of forecasting performance.

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